The role of artificial intelligence in medicine is growing because its capacities are not limited to diagnosing diseases. Accenture mentions a CAGR of 40% for AI in the healthcare market. From 2014 to 2021, it increased from $600 million to $6.6 billion, and by 2025 it will grow almost five times. However, organizations are slow to implement intelligent algorithms in medical applications due to various obstacles. Let’s consider the problems that hinder the implementation of this technology in healthcare and ways to solve them.
Problems of implementing AI in healthcare
Today, AI helps doctors operate on patients, remotely monitor them, diagnose diseases at an early stage, and check laboratory tests and medical images. The benefits of this technology are clear, but there are some ethical and legal issues:
- healthcare professionals must know in what cases they need to get consent from patients for the use of AI;
- it is still unclear to what extent doctors are responsible for errors in the work of intelligent algorithms;
- doctors don’t know how AI functions and cannot explain its recommendations;
- the safety and effectiveness of AI for the treatment of patients are questionable;
- people do not know how reliable AI data are and in what cases they can be withdrawn or deleted;
- it is difficult for doctors to delegate tasks to AI;
- doctors must know what document contains the rules and conditions for the use of AI in medical institutions;
- there are not enough technical specialists who know how to work with AI and solve emerging issues.
To some extent, a healthcare software development company can help a medical institution deal with these issues. Of course, it won’t create ethical and legal norms for the use of AI. But it can prevent problems by creating high-quality AI applications.
How to seamlessly implement AI in healthcare
To implement a properly functioning, efficient, and secure AI application into the work of a medical organization, a company should follow these recommendations:
1. Define AI use cases
AI can be used for many purposes, from managing administrative procedures to patient care. It is difficult and expensive to introduce technology into all processes at once. A medical institution should decide on the priority use case.
If your healthcare facility is implementing AI, you should start small. A simple AI-powered application will help medical staff get familiar with the technology. Healthcare professionals will appreciate the advantages of an intelligent algorithm, “get used” to an AI program and learn to delegate tasks to it. When employees master AI skills, your clinic can move on to more complex AI projects.
Before starting a project, establish a shared vision with stakeholders. You should decide on how your organization will use AI, what business problems the technology will solve, and what the expected ROI is. Without agreeing on these critical points, you’ll have difficulty drawing up requirements for your software product. Discrepancies and different perceptions of success criteria can hinder project implementation and slow down healthcare software development. Preliminary approval will help you plan project work and bring your AI application into production on time.
2. Prepare qualitative data for AI training
Most of the work with AI is data preparation. An algorithm cannot function accurately and correctly without a quality base. A special team of experts collects, profiles, and prepares information. As a rule, medical institutions don’t have in-house data specialists, so they outsource these tasks.
Developers create a machine learning model and train it using an ML engine. They evaluate how well it copes with real tasks. They monitor the performance of the model to prevent data drift in time.
Confidential patient information must be anonymous so no one can identify the owner. When creating healthcare AI applications, business owners must protect user rights. Consultants on legal issues of healthcare software development will help you comply with the law.
3. Recruit experienced developers
The traditional programming skills are not enough to develop healthcare AI applications. You should search for specialists who know how to work with AI and build healthcare applications.
If you have difficulty picking up specialists, turn to outsource IT companies specializing in AI. It is important to study the list of AI projects implemented by a company. The candidate could have already created solutions similar to the one your clinic intends to implement. For example, if you need a remote patient monitoring system, you can choose a team experienced in developing virtual assistants. Such programmers know the pitfalls of development and can implement your project faster than a team that creates such a software solution for the first time. It’s always easier to keep to the beaten track than create a totally new product.
Outsourcing development to an experienced IT team will help your medical institution save time and money.
Conclusion
Creating and implementing AI in healthcare is not an easy but promising task. Accenture estimates that AI could save the industry $150 billion annually through faster operations and personalized, timely patient care.
To get started, you should contact a healthcare software development company. Such an IT partner will discover business problems and suggest AI use cases. It will develop the necessary software and help you implement it in your organization, considering relevant legal and ethical issues.
